---
license: apache-2.0
library_name: transformers
model_size: 42B
language:
- en
- fr
- zh
- de
tags:
- quantized
- gptq
- w4a16
- llm-compressor
- qwen3
- mixture-of-experts
- coding
- programming
- code generation
- code
- codeqwen
- moe
- coder
- qwen2
- chat
- qwen
- qwen-coder
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 8 active experts
- 512k context
- finetune
- brainstorm 20x
- brainstorm
- optional thinking
- qwen3_moe
- rocm
- amd
- r9700
- RDNA4
- gfx1201
- ultra quality
- llama-cpp
- gguf-my-repo
base_model: tcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16
pipeline_tag: text-generation
---

# BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF
This model was converted to GGUF format from [`tcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16`](https://huggingface.co/tcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/tcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -c 2048
```